In CT scanner systems, X-ray detectors have changed over decades. For the purpose of acquiring two dimensional projection data, the detectors are arranged in two dimensions that include rows and columns. In general, each of the rows is sequentially read to detect X-ray, and each sequential reading takes a known amount of time that becomes a delay or a lag. Despite the sequential reading, since the X-ray detector has increased a number of rows to cover a wider scanning area, the time delay has accumulated over the rows to become some concern.
In particular, to scan a wider area of a subject, a number of rows of the detectors has dramatically increased from 16 to 320 over years. For example, Toshiba Aquilion ONE™ currently is equipped with 320 rows of the detectors and covers 160 mm of the subject area as the detector set completes one rotation around the subject in 0.35 second. While the number of the detector rows has increased, the rotational speed of the gantry has also improved. Both the expanded rows of the detector and the high rotating speed require the detectors to improve their detection efficiency.
As a number of rows of the detectors increases, the detection speed becomes a significant factor as the detectors are rotating at a high speed. This is because the detectors must detect their inputs with the least amount of time delay so that projection data contains substantially minimal error due to a detection delay. Ultimately, an image should be reconstructed from the above described projection data. Since the detection delay in the data acquisition system (DAS) typically ranges from three to six micro seconds for each row of the detectors, if the total number of rows is less than 16 rows, the delay may not cause noticeable artifact in the reconstructed image. On the other hand, the time delay artifact may become noticeable when the number of detector rows is more than a certain number such as 16. For example, in the most extreme case, when 320 rows of detectors are used with a 3 micro-second delay, the detector delay progressively becomes worse as each row outputs its signals and the last row or 320th row could have a 920 micro-second delay.
Artifact also becomes a significant issue when the above described detection delay is combined with other factors such as an acquisition method and a reconstruction algorithm. One example of the combination is that the detector delay contributes to the artifact when the reconstruction is performed on helical projection data using a certain algorithm as in the case of “Exact Reconstruction” of Katsevich type. Another example of the combination is that even if a number of the detector rows is relatively small, the artifacts becomes a significant issue for reconstruction under flying focal spot (FFS).
In response to the above described problems, one prior art attempt is to improve the response characteristics of the detectors. Although this approach has been pursued at additional hardware costs, the associated costs may be practically prohibitive. Furthermore, the approach will never be perfect since the detectors are also moving at a high speed during the scan. Thus, it remains desirable to improve the image quality by substantially reducing the artifact due to the DAS lag without using additional hardware solution.